#include <vector>
#include <iterator>

#include <ORBextractor.h>
#include <cmath>


const int PATCH_SIZE = 31;
const int HALF_PATCH_SIZE = 15;
const int EDGE_THRESHOLD = 19;


static float IC_Angle(const cv::Mat& image, cv::Point2f pt, const std::vector<int> & u_max)
{
	int m_01 = 0, m_10 = 0;

	const uchar* center = &image.at<uchar>(cvRound(pt.y), cvRound(pt.x));

	// Treat the center line differently, v=0
	for (int u = -HALF_PATCH_SIZE; u <= HALF_PATCH_SIZE; ++u)
		m_10 += u * center[u];

	// Go line by line in the circuI853lar patch
	int step = (int)image.step1();
	for (int v = 1; v <= HALF_PATCH_SIZE; ++v)
	{
		// Proceed over the two lines
		int v_sum = 0;
		int d = u_max[v];
		for (int u = -d; u <= d; ++u)
		{
			int val_plus = center[u + v*step], val_minus = center[u - v*step];
			v_sum += (val_plus - val_minus);
			m_10 += u * (val_plus + val_minus);
		}
		m_01 += v * v_sum;
	}

	return cv::fastAtan2((float)m_01, (float)m_10);
}


const float factorPI = (float)(CV_PI / 180.f);
static void computeOrbDescriptor(const cv::KeyPoint& kpt,
	const cv::Mat& img, const cv::Point* pattern,
	uchar* desc)
{
	float angle = (float)kpt.angle*factorPI;
	float a = (float)cos(angle), b = (float)sin(angle);

	const uchar* center = &img.at<uchar>(cvRound(kpt.pt.y), cvRound(kpt.pt.x));
	const int step = (int)img.step;

#define GET_VALUE(idx) \
    center[cvRound(pattern[idx].x*b + pattern[idx].y*a)*step + \
            cvRound(pattern[idx].x*a - pattern[idx].y*b)]


	for (int i = 0; i < 32; ++i, pattern += 16)
	{
		int t0, t1, val;
		t0 = GET_VALUE(0); t1 = GET_VALUE(1);
		val = t0 < t1;
		t0 = GET_VALUE(2); t1 = GET_VALUE(3);
		val |= (t0 < t1) << 1;
		t0 = GET_VALUE(4); t1 = GET_VALUE(5);
		val |= (t0 < t1) << 2;
		t0 = GET_VALUE(6); t1 = GET_VALUE(7);
		val |= (t0 < t1) << 3;
		t0 = GET_VALUE(8); t1 = GET_VALUE(9);
		val |= (t0 < t1) << 4;
		t0 = GET_VALUE(10); t1 = GET_VALUE(11);
		val |= (t0 < t1) << 5;
		t0 = GET_VALUE(12); t1 = GET_VALUE(13);
		val |= (t0 < t1) << 6;
		t0 = GET_VALUE(14); t1 = GET_VALUE(15);
		val |= (t0 < t1) << 7;

		desc[i] = (uchar)val;
	}

#undef GET_VALUE
}


static int bit_pattern_31_[256 * 4] =
{
	8,-3, 9,5/*mean (0), correlation (0)*/,
	4,2, 7,-12/*mean (1.12461e-05), correlation (0.0437584)*/,
	-11,9, -8,2/*mean (3.37382e-05), correlation (0.0617409)*/,
	7,-12, 12,-13/*mean (5.62303e-05), correlation (0.0636977)*/,
	2,-13, 2,12/*mean (0.000134953), correlation (0.085099)*/,
	1,-7, 1,6/*mean (0.000528565), correlation (0.0857175)*/,
	-2,-10, -2,-4/*mean (0.0188821), correlation (0.0985774)*/,
	-13,-13, -11,-8/*mean (0.0363135), correlation (0.0899616)*/,
	-13,-3, -12,-9/*mean (0.121806), correlation (0.099849)*/,
	10,4, 11,9/*mean (0.122065), correlation (0.093285)*/,
	-13,-8, -8,-9/*mean (0.162787), correlation (0.0942748)*/,
	-11,7, -9,12/*mean (0.21561), correlation (0.0974438)*/,
	7,7, 12,6/*mean (0.160583), correlation (0.130064)*/,
	-4,-5, -3,0/*mean (0.228171), correlation (0.132998)*/,
	-13,2, -12,-3/*mean (0.00997526), correlation (0.145926)*/,
	-9,0, -7,5/*mean (0.198234), correlation (0.143636)*/,
	12,-6, 12,-1/*mean (0.0676226), correlation (0.16689)*/,
	-3,6, -2,12/*mean (0.166847), correlation (0.171682)*/,
	-6,-13, -4,-8/*mean (0.101215), correlation (0.179716)*/,
	11,-13, 12,-8/*mean (0.200641), correlation (0.192279)*/,
	4,7, 5,1/*mean (0.205106), correlation (0.186848)*/,
	5,-3, 10,-3/*mean (0.234908), correlation (0.192319)*/,
	3,-7, 6,12/*mean (0.0709964), correlation (0.210872)*/,
	-8,-7, -6,-2/*mean (0.0939834), correlation (0.212589)*/,
	-2,11, -1,-10/*mean (0.127778), correlation (0.20866)*/,
	-13,12, -8,10/*mean (0.14783), correlation (0.206356)*/,
	-7,3, -5,-3/*mean (0.182141), correlation (0.198942)*/,
	-4,2, -3,7/*mean (0.188237), correlation (0.21384)*/,
	-10,-12, -6,11/*mean (0.14865), correlation (0.23571)*/,
	5,-12, 6,-7/*mean (0.222312), correlation (0.23324)*/,
	5,-6, 7,-1/*mean (0.229082), correlation (0.23389)*/,
	1,0, 4,-5/*mean (0.241577), correlation (0.215286)*/,
	9,11, 11,-13/*mean (0.00338507), correlation (0.251373)*/,
	4,7, 4,12/*mean (0.131005), correlation (0.257622)*/,
	2,-1, 4,4/*mean (0.152755), correlation (0.255205)*/,
	-4,-12, -2,7/*mean (0.182771), correlation (0.244867)*/,
	-8,-5, -7,-10/*mean (0.186898), correlation (0.23901)*/,
	4,11, 9,12/*mean (0.226226), correlation (0.258255)*/,
	0,-8, 1,-13/*mean (0.0897886), correlation (0.274827)*/,
	-13,-2, -8,2/*mean (0.148774), correlation (0.28065)*/,
	-3,-2, -2,3/*mean (0.153048), correlation (0.283063)*/,
	-6,9, -4,-9/*mean (0.169523), correlation (0.278248)*/,
	8,12, 10,7/*mean (0.225337), correlation (0.282851)*/,
	0,9, 1,3/*mean (0.226687), correlation (0.278734)*/,
	7,-5, 11,-10/*mean (0.00693882), correlation (0.305161)*/,
	-13,-6, -11,0/*mean (0.0227283), correlation (0.300181)*/,
	10,7, 12,1/*mean (0.125517), correlation (0.31089)*/,
	-6,-3, -6,12/*mean (0.131748), correlation (0.312779)*/,
	10,-9, 12,-4/*mean (0.144827), correlation (0.292797)*/,
	-13,8, -8,-12/*mean (0.149202), correlation (0.308918)*/,
	-13,0, -8,-4/*mean (0.160909), correlation (0.310013)*/,
	3,3, 7,8/*mean (0.177755), correlation (0.309394)*/,
	5,7, 10,-7/*mean (0.212337), correlation (0.310315)*/,
	-1,7, 1,-12/*mean (0.214429), correlation (0.311933)*/,
	3,-10, 5,6/*mean (0.235807), correlation (0.313104)*/,
	2,-4, 3,-10/*mean (0.00494827), correlation (0.344948)*/,
	-13,0, -13,5/*mean (0.0549145), correlation (0.344675)*/,
	-13,-7, -12,12/*mean (0.103385), correlation (0.342715)*/,
	-13,3, -11,8/*mean (0.134222), correlation (0.322922)*/,
	-7,12, -4,7/*mean (0.153284), correlation (0.337061)*/,
	6,-10, 12,8/*mean (0.154881), correlation (0.329257)*/,
	-9,-1, -7,-6/*mean (0.200967), correlation (0.33312)*/,
	-2,-5, 0,12/*mean (0.201518), correlation (0.340635)*/,
	-12,5, -7,5/*mean (0.207805), correlation (0.335631)*/,
	3,-10, 8,-13/*mean (0.224438), correlation (0.34504)*/,
	-7,-7, -4,5/*mean (0.239361), correlation (0.338053)*/,
	-3,-2, -1,-7/*mean (0.240744), correlation (0.344322)*/,
	2,9, 5,-11/*mean (0.242949), correlation (0.34145)*/,
	-11,-13, -5,-13/*mean (0.244028), correlation (0.336861)*/,
	-1,6, 0,-1/*mean (0.247571), correlation (0.343684)*/,
	5,-3, 5,2/*mean (0.000697256), correlation (0.357265)*/,
	-4,-13, -4,12/*mean (0.00213675), correlation (0.373827)*/,
	-9,-6, -9,6/*mean (0.0126856), correlation (0.373938)*/,
	-12,-10, -8,-4/*mean (0.0152497), correlation (0.364237)*/,
	10,2, 12,-3/*mean (0.0299933), correlation (0.345292)*/,
	7,12, 12,12/*mean (0.0307242), correlation (0.366299)*/,
	-7,-13, -6,5/*mean (0.0534975), correlation (0.368357)*/,
	-4,9, -3,4/*mean (0.099865), correlation (0.372276)*/,
	7,-1, 12,2/*mean (0.117083), correlation (0.364529)*/,
	-7,6, -5,1/*mean (0.126125), correlation (0.369606)*/,
	-13,11, -12,5/*mean (0.130364), correlation (0.358502)*/,
	-3,7, -2,-6/*mean (0.131691), correlation (0.375531)*/,
	7,-8, 12,-7/*mean (0.160166), correlation (0.379508)*/,
	-13,-7, -11,-12/*mean (0.167848), correlation (0.353343)*/,
	1,-3, 12,12/*mean (0.183378), correlation (0.371916)*/,
	2,-6, 3,0/*mean (0.228711), correlation (0.371761)*/,
	-4,3, -2,-13/*mean (0.247211), correlation (0.364063)*/,
	-1,-13, 1,9/*mean (0.249325), correlation (0.378139)*/,
	7,1, 8,-6/*mean (0.000652272), correlation (0.411682)*/,
	1,-1, 3,12/*mean (0.00248538), correlation (0.392988)*/,
	9,1, 12,6/*mean (0.0206815), correlation (0.386106)*/,
	-1,-9, -1,3/*mean (0.0364485), correlation (0.410752)*/,
	-13,-13, -10,5/*mean (0.0376068), correlation (0.398374)*/,
	7,7, 10,12/*mean (0.0424202), correlation (0.405663)*/,
	12,-5, 12,9/*mean (0.0942645), correlation (0.410422)*/,
	6,3, 7,11/*mean (0.1074), correlation (0.413224)*/,
	5,-13, 6,10/*mean (0.109256), correlation (0.408646)*/,
	2,-12, 2,3/*mean (0.131691), correlation (0.416076)*/,
	3,8, 4,-6/*mean (0.165081), correlation (0.417569)*/,
	2,6, 12,-13/*mean (0.171874), correlation (0.408471)*/,
	9,-12, 10,3/*mean (0.175146), correlation (0.41296)*/,
	-8,4, -7,9/*mean (0.183682), correlation (0.402956)*/,
	-11,12, -4,-6/*mean (0.184672), correlation (0.416125)*/,
	1,12, 2,-8/*mean (0.191487), correlation (0.386696)*/,
	6,-9, 7,-4/*mean (0.192668), correlation (0.394771)*/,
	2,3, 3,-2/*mean (0.200157), correlation (0.408303)*/,
	6,3, 11,0/*mean (0.204588), correlation (0.411762)*/,
	3,-3, 8,-8/*mean (0.205904), correlation (0.416294)*/,
	7,8, 9,3/*mean (0.213237), correlation (0.409306)*/,
	-11,-5, -6,-4/*mean (0.243444), correlation (0.395069)*/,
	-10,11, -5,10/*mean (0.247672), correlation (0.413392)*/,
	-5,-8, -3,12/*mean (0.24774), correlation (0.411416)*/,
	-10,5, -9,0/*mean (0.00213675), correlation (0.454003)*/,
	8,-1, 12,-6/*mean (0.0293635), correlation (0.455368)*/,
	4,-6, 6,-11/*mean (0.0404971), correlation (0.457393)*/,
	-10,12, -8,7/*mean (0.0481107), correlation (0.448364)*/,
	4,-2, 6,7/*mean (0.050641), correlation (0.455019)*/,
	-2,0, -2,12/*mean (0.0525978), correlation (0.44338)*/,
	-5,-8, -5,2/*mean (0.0629667), correlation (0.457096)*/,
	7,-6, 10,12/*mean (0.0653846), correlation (0.445623)*/,
	-9,-13, -8,-8/*mean (0.0858749), correlation (0.449789)*/,
	-5,-13, -5,-2/*mean (0.122402), correlation (0.450201)*/,
	8,-8, 9,-13/*mean (0.125416), correlation (0.453224)*/,
	-9,-11, -9,0/*mean (0.130128), correlation (0.458724)*/,
	1,-8, 1,-2/*mean (0.132467), correlation (0.440133)*/,
	7,-4, 9,1/*mean (0.132692), correlation (0.454)*/,
	-2,1, -1,-4/*mean (0.135695), correlation (0.455739)*/,
	11,-6, 12,-11/*mean (0.142904), correlation (0.446114)*/,
	-12,-9, -6,4/*mean (0.146165), correlation (0.451473)*/,
	3,7, 7,12/*mean (0.147627), correlation (0.456643)*/,
	5,5, 10,8/*mean (0.152901), correlation (0.455036)*/,
	0,-4, 2,8/*mean (0.167083), correlation (0.459315)*/,
	-9,12, -5,-13/*mean (0.173234), correlation (0.454706)*/,
	0,7, 2,12/*mean (0.18312), correlation (0.433855)*/,
	-1,2, 1,7/*mean (0.185504), correlation (0.443838)*/,
	5,11, 7,-9/*mean (0.185706), correlation (0.451123)*/,
	3,5, 6,-8/*mean (0.188968), correlation (0.455808)*/,
	-13,-4, -8,9/*mean (0.191667), correlation (0.459128)*/,
	-5,9, -3,-3/*mean (0.193196), correlation (0.458364)*/,
	-4,-7, -3,-12/*mean (0.196536), correlation (0.455782)*/,
	6,5, 8,0/*mean (0.1972), correlation (0.450481)*/,
	-7,6, -6,12/*mean (0.199438), correlation (0.458156)*/,
	-13,6, -5,-2/*mean (0.211224), correlation (0.449548)*/,
	1,-10, 3,10/*mean (0.211718), correlation (0.440606)*/,
	4,1, 8,-4/*mean (0.213034), correlation (0.443177)*/,
	-2,-2, 2,-13/*mean (0.234334), correlation (0.455304)*/,
	2,-12, 12,12/*mean (0.235684), correlation (0.443436)*/,
	-2,-13, 0,-6/*mean (0.237674), correlation (0.452525)*/,
	4,1, 9,3/*mean (0.23962), correlation (0.444824)*/,
	-6,-10, -3,-5/*mean (0.248459), correlation (0.439621)*/,
	-3,-13, -1,1/*mean (0.249505), correlation (0.456666)*/,
	7,5, 12,-11/*mean (0.00119208), correlation (0.495466)*/,
	4,-2, 5,-7/*mean (0.00372245), correlation (0.484214)*/,
	-13,9, -9,-5/*mean (0.00741116), correlation (0.499854)*/,
	7,1, 8,6/*mean (0.0208952), correlation (0.499773)*/,
	7,-8, 7,6/*mean (0.0220085), correlation (0.501609)*/,
	-7,-4, -7,1/*mean (0.0233806), correlation (0.496568)*/,
	-8,11, -7,-8/*mean (0.0236505), correlation (0.489719)*/,
	-13,6, -12,-8/*mean (0.0268781), correlation (0.503487)*/,
	2,4, 3,9/*mean (0.0323324), correlation (0.501938)*/,
	10,-5, 12,3/*mean (0.0399235), correlation (0.494029)*/,
	-6,-5, -6,7/*mean (0.0420153), correlation (0.486579)*/,
	8,-3, 9,-8/*mean (0.0548021), correlation (0.484237)*/,
	2,-12, 2,8/*mean (0.0616622), correlation (0.496642)*/,
	-11,-2, -10,3/*mean (0.0627755), correlation (0.498563)*/,
	-12,-13, -7,-9/*mean (0.0829622), correlation (0.495491)*/,
	-11,0, -10,-5/*mean (0.0843342), correlation (0.487146)*/,
	5,-3, 11,8/*mean (0.0929937), correlation (0.502315)*/,
	-2,-13, -1,12/*mean (0.113327), correlation (0.48941)*/,
	-1,-8, 0,9/*mean (0.132119), correlation (0.467268)*/,
	-13,-11, -12,-5/*mean (0.136269), correlation (0.498771)*/,
	-10,-2, -10,11/*mean (0.142173), correlation (0.498714)*/,
	-3,9, -2,-13/*mean (0.144141), correlation (0.491973)*/,
	2,-3, 3,2/*mean (0.14892), correlation (0.500782)*/,
	-9,-13, -4,0/*mean (0.150371), correlation (0.498211)*/,
	-4,6, -3,-10/*mean (0.152159), correlation (0.495547)*/,
	-4,12, -2,-7/*mean (0.156152), correlation (0.496925)*/,
	-6,-11, -4,9/*mean (0.15749), correlation (0.499222)*/,
	6,-3, 6,11/*mean (0.159211), correlation (0.503821)*/,
	-13,11, -5,5/*mean (0.162427), correlation (0.501907)*/,
	11,11, 12,6/*mean (0.16652), correlation (0.497632)*/,
	7,-5, 12,-2/*mean (0.169141), correlation (0.484474)*/,
	-1,12, 0,7/*mean (0.169456), correlation (0.495339)*/,
	-4,-8, -3,-2/*mean (0.171457), correlation (0.487251)*/,
	-7,1, -6,7/*mean (0.175), correlation (0.500024)*/,
	-13,-12, -8,-13/*mean (0.175866), correlation (0.497523)*/,
	-7,-2, -6,-8/*mean (0.178273), correlation (0.501854)*/,
	-8,5, -6,-9/*mean (0.181107), correlation (0.494888)*/,
	-5,-1, -4,5/*mean (0.190227), correlation (0.482557)*/,
	-13,7, -8,10/*mean (0.196739), correlation (0.496503)*/,
	1,5, 5,-13/*mean (0.19973), correlation (0.499759)*/,
	1,0, 10,-13/*mean (0.204465), correlation (0.49873)*/,
	9,12, 10,-1/*mean (0.209334), correlation (0.49063)*/,
	5,-8, 10,-9/*mean (0.211134), correlation (0.503011)*/,
	-1,11, 1,-13/*mean (0.212), correlation (0.499414)*/,
	-9,-3, -6,2/*mean (0.212168), correlation (0.480739)*/,
	-1,-10, 1,12/*mean (0.212731), correlation (0.502523)*/,
	-13,1, -8,-10/*mean (0.21327), correlation (0.489786)*/,
	8,-11, 10,-6/*mean (0.214159), correlation (0.488246)*/,
	2,-13, 3,-6/*mean (0.216993), correlation (0.50287)*/,
	7,-13, 12,-9/*mean (0.223639), correlation (0.470502)*/,
	-10,-10, -5,-7/*mean (0.224089), correlation (0.500852)*/,
	-10,-8, -8,-13/*mean (0.228666), correlation (0.502629)*/,
	4,-6, 8,5/*mean (0.22906), correlation (0.498305)*/,
	3,12, 8,-13/*mean (0.233378), correlation (0.503825)*/,
	-4,2, -3,-3/*mean (0.234323), correlation (0.476692)*/,
	5,-13, 10,-12/*mean (0.236392), correlation (0.475462)*/,
	4,-13, 5,-1/*mean (0.236842), correlation (0.504132)*/,
	-9,9, -4,3/*mean (0.236977), correlation (0.497739)*/,
	0,3, 3,-9/*mean (0.24314), correlation (0.499398)*/,
	-12,1, -6,1/*mean (0.243297), correlation (0.489447)*/,
	3,2, 4,-8/*mean (0.00155196), correlation (0.553496)*/,
	-10,-10, -10,9/*mean (0.00239541), correlation (0.54297)*/,
	8,-13, 12,12/*mean (0.0034413), correlation (0.544361)*/,
	-8,-12, -6,-5/*mean (0.003565), correlation (0.551225)*/,
	2,2, 3,7/*mean (0.00835583), correlation (0.55285)*/,
	10,6, 11,-8/*mean (0.00885065), correlation (0.540913)*/,
	6,8, 8,-12/*mean (0.0101552), correlation (0.551085)*/,
	-7,10, -6,5/*mean (0.0102227), correlation (0.533635)*/,
	-3,-9, -3,9/*mean (0.0110211), correlation (0.543121)*/,
	-1,-13, -1,5/*mean (0.0113473), correlation (0.550173)*/,
	-3,-7, -3,4/*mean (0.0140913), correlation (0.554774)*/,
	-8,-2, -8,3/*mean (0.017049), correlation (0.55461)*/,
	4,2, 12,12/*mean (0.01778), correlation (0.546921)*/,
	2,-5, 3,11/*mean (0.0224022), correlation (0.549667)*/,
	6,-9, 11,-13/*mean (0.029161), correlation (0.546295)*/,
	3,-1, 7,12/*mean (0.0303081), correlation (0.548599)*/,
	11,-1, 12,4/*mean (0.0355151), correlation (0.523943)*/,
	-3,0, -3,6/*mean (0.0417904), correlation (0.543395)*/,
	4,-11, 4,12/*mean (0.0487292), correlation (0.542818)*/,
	2,-4, 2,1/*mean (0.0575124), correlation (0.554888)*/,
	-10,-6, -8,1/*mean (0.0594242), correlation (0.544026)*/,
	-13,7, -11,1/*mean (0.0597391), correlation (0.550524)*/,
	-13,12, -11,-13/*mean (0.0608974), correlation (0.55383)*/,
	6,0, 11,-13/*mean (0.065126), correlation (0.552006)*/,
	0,-1, 1,4/*mean (0.074224), correlation (0.546372)*/,
	-13,3, -9,-2/*mean (0.0808592), correlation (0.554875)*/,
	-9,8, -6,-3/*mean (0.0883378), correlation (0.551178)*/,
	-13,-6, -8,-2/*mean (0.0901035), correlation (0.548446)*/,
	5,-9, 8,10/*mean (0.0949843), correlation (0.554694)*/,
	2,7, 3,-9/*mean (0.0994152), correlation (0.550979)*/,
	-1,-6, -1,-1/*mean (0.10045), correlation (0.552714)*/,
	9,5, 11,-2/*mean (0.100686), correlation (0.552594)*/,
	11,-3, 12,-8/*mean (0.101091), correlation (0.532394)*/,
	3,0, 3,5/*mean (0.101147), correlation (0.525576)*/,
	-1,4, 0,10/*mean (0.105263), correlation (0.531498)*/,
	3,-6, 4,5/*mean (0.110785), correlation (0.540491)*/,
	-13,0, -10,5/*mean (0.112798), correlation (0.536582)*/,
	5,8, 12,11/*mean (0.114181), correlation (0.555793)*/,
	8,9, 9,-6/*mean (0.117431), correlation (0.553763)*/,
	7,-4, 8,-12/*mean (0.118522), correlation (0.553452)*/,
	-10,4, -10,9/*mean (0.12094), correlation (0.554785)*/,
	7,3, 12,4/*mean (0.122582), correlation (0.555825)*/,
	9,-7, 10,-2/*mean (0.124978), correlation (0.549846)*/,
	7,0, 12,-2/*mean (0.127002), correlation (0.537452)*/,
	-1,-6, 0,-11/*mean (0.127148), correlation (0.547401)*/
};

ORBextractor::ORBextractor(int _nfeatures, float _scaleFactor, int _nlevels,
	int _iniThFAST, int _minThFAST) :
	nfeatures(_nfeatures), scaleFactor(_scaleFactor), nlevels(_nlevels),
	iniThFAST(_iniThFAST), minThFAST(_minThFAST)
{
	mvScaleFactor.resize(nlevels);
	mvLevelSigma2.resize(nlevels);
	mvScaleFactor[0] = 1.0f;
	mvLevelSigma2[0] = 1.0f;
	for (int i = 1; i<nlevels; i++)
	{
		mvScaleFactor[i] = mvScaleFactor[i - 1] * scaleFactor;
		mvLevelSigma2[i] = mvScaleFactor[i] * mvScaleFactor[i];
	}

	mvInvScaleFactor.resize(nlevels);
	mvInvLevelSigma2.resize(nlevels);
	for (int i = 0; i<nlevels; i++)
	{
		mvInvScaleFactor[i] = 1.0f / mvScaleFactor[i];
		mvInvLevelSigma2[i] = 1.0f / mvLevelSigma2[i];
	}

	mvImagePyramid.resize(nlevels);

	mnFeaturesPerLevel.resize(nlevels);
	float factor = 1.0f / scaleFactor;
	float nDesiredFeaturesPerScale = nfeatures*(1 - factor) / (1 - (float)pow((double)factor, (double)nlevels));

	int sumFeatures = 0;
	for (int level = 0; level < nlevels - 1; level++)
	{
		mnFeaturesPerLevel[level] = cvRound(nDesiredFeaturesPerScale);
		sumFeatures += mnFeaturesPerLevel[level];
		nDesiredFeaturesPerScale *= factor;
	}
	mnFeaturesPerLevel[nlevels - 1] = std::max(nfeatures - sumFeatures, 0);

	const int npoints = 512;
	const cv::Point* pattern0 = (const cv::Point*)bit_pattern_31_;
	std::copy(pattern0, pattern0 + npoints, std::back_inserter(pattern));

	//This is for orientation
	// pre-compute the end of a row in a circular patch
	umax.resize(HALF_PATCH_SIZE + 1);

	int v, v0, vmax = cvFloor(HALF_PATCH_SIZE * sqrt(2.f) / 2 + 1);
	int vmin = cvCeil(HALF_PATCH_SIZE * sqrt(2.f) / 2);
	const double hp2 = HALF_PATCH_SIZE*HALF_PATCH_SIZE;
	for (v = 0; v <= vmax; ++v)
		umax[v] = cvRound(sqrt(hp2 - v * v));

	// Make sure we are symmetric
	for (v = HALF_PATCH_SIZE, v0 = 0; v >= vmin; --v)
	{
		while (umax[v0] == umax[v0 + 1])
			++v0;
		umax[v] = v0;
		++v0;
	}
}

static void computeOrientation(const cv::Mat& image, std::vector<cv::KeyPoint>& keypoints, const std::vector<int>& umax)
{
	for (std::vector<cv::KeyPoint>::iterator keypoint = keypoints.begin(),
		keypointEnd = keypoints.end(); keypoint != keypointEnd; ++keypoint)
	{
		keypoint->angle = IC_Angle(image, keypoint->pt, umax);
	}
}

void ExtractorNode::DivideNode(ExtractorNode &n1, ExtractorNode &n2, ExtractorNode &n3, ExtractorNode &n4)
{
	const int halfX = ceil(static_cast<float>(UR.x - UL.x) / 2);
	const int halfY = ceil(static_cast<float>(BR.y - UL.y) / 2);

	//Define boundaries of childs
	n1.UL = UL;
	n1.UR = cv::Point2i(UL.x + halfX, UL.y);
	n1.BL = cv::Point2i(UL.x, UL.y + halfY);
	n1.BR = cv::Point2i(UL.x + halfX, UL.y + halfY);
	n1.vKeys.reserve(vKeys.size());

	n2.UL = n1.UR;
	n2.UR = UR;
	n2.BL = n1.BR;
	n2.BR = cv::Point2i(UR.x, UL.y + halfY);
	n2.vKeys.reserve(vKeys.size());

	n3.UL = n1.BL;
	n3.UR = n1.BR;
	n3.BL = BL;
	n3.BR = cv::Point2i(n1.BR.x, BL.y);
	n3.vKeys.reserve(vKeys.size());

	n4.UL = n3.UR;
	n4.UR = n2.BR;
	n4.BL = n3.BR;
	n4.BR = BR;
	n4.vKeys.reserve(vKeys.size());

	//Associate points to childs
	for (size_t i = 0; i<vKeys.size(); i++)
	{
		const cv::KeyPoint &kp = vKeys[i];
		if (kp.pt.x<n1.UR.x)
		{
			if (kp.pt.y<n1.BR.y)
				n1.vKeys.push_back(kp);
			else
				n3.vKeys.push_back(kp);
		}
		else if (kp.pt.y<n1.BR.y)
			n2.vKeys.push_back(kp);
		else
			n4.vKeys.push_back(kp);
	}

	if (n1.vKeys.size() == 1)
		n1.bNoMore = true;
	if (n2.vKeys.size() == 1)
		n2.bNoMore = true;
	if (n3.vKeys.size() == 1)
		n3.bNoMore = true;
	if (n4.vKeys.size() == 1)
		n4.bNoMore = true;

}

std::vector<cv::KeyPoint> ORBextractor::DistributeOctTree(const std::vector<cv::KeyPoint>& vToDistributeKeys, const int &minX,
	const int &maxX, const int &minY, const int &maxY, const int &N, const int &level)
{
	// Compute how many initial nodes  
	
	const int nInic = cvRound(static_cast<float>(maxX - minX) / (maxY - minY));
	const int nInir = cvRound(static_cast<float>(maxY - minY) / (maxX - minX));

	const int nIni = nInic == 0 ? nInir : nInic;
	const float hX = static_cast<float>(maxX - minX) / nIni;

	std::list<ExtractorNode> lNodes;

	std::vector<ExtractorNode*> vpIniNodes;
	vpIniNodes.resize(nIni);

	for (int i = 0; i<nIni; i++)
	{
		ExtractorNode ni;
		ni.UL = cv::Point2i(hX*static_cast<float>(i), 0);
		ni.UR = cv::Point2i(hX*static_cast<float>(i + 1), 0);
		ni.BL = cv::Point2i(ni.UL.x, maxY - minY);
		ni.BR = cv::Point2i(ni.UR.x, maxY - minY);
		ni.vKeys.reserve(vToDistributeKeys.size());

		lNodes.push_back(ni);
		vpIniNodes[i] = &lNodes.back();
	}

	//Associate points to childs
	for (size_t i = 0; i<vToDistributeKeys.size(); i++)
	{
		const cv::KeyPoint &kp = vToDistributeKeys[i];
		vpIniNodes[kp.pt.x / hX]->vKeys.push_back(kp);
	}

	std::list<ExtractorNode>::iterator lit = lNodes.begin();

	while (lit != lNodes.end())
	{
		if (lit->vKeys.size() == 1)
		{
			lit->bNoMore = true;
			lit++;
		}
		else if (lit->vKeys.empty())
			lit = lNodes.erase(lit);
		else
			lit++;
	}

	bool bFinish = false;

	int iteration = 0;

	std::vector<std::pair<int, ExtractorNode*> > vSizeAndPointerToNode;
	vSizeAndPointerToNode.reserve(lNodes.size() * 4);

	while (!bFinish)
	{
		iteration++;

		int prevSize = lNodes.size();

		lit = lNodes.begin();

		int nToExpand = 0;

		vSizeAndPointerToNode.clear();

		while (lit != lNodes.end())
		{
			if (lit->bNoMore)
			{
				// If node only contains one point do not subdivide and continue
				lit++;
				continue;
			}
			else
			{
				// If more than one point, subdivide
				ExtractorNode n1, n2, n3, n4;
				lit->DivideNode(n1, n2, n3, n4);

				// Add childs if they contain points
				if (n1.vKeys.size()>0)
				{
					lNodes.push_front(n1);
					if (n1.vKeys.size()>1)
					{
						nToExpand++;
						vSizeAndPointerToNode.push_back(std::make_pair(n1.vKeys.size(), &lNodes.front()));
						lNodes.front().lit = lNodes.begin();
					}
				}
				if (n2.vKeys.size()>0)
				{
					lNodes.push_front(n2);
					if (n2.vKeys.size()>1)
					{
						nToExpand++;
						vSizeAndPointerToNode.push_back(std::make_pair(n2.vKeys.size(), &lNodes.front()));
						lNodes.front().lit = lNodes.begin();
					}
				}
				if (n3.vKeys.size()>0)
				{
					lNodes.push_front(n3);
					if (n3.vKeys.size()>1)
					{
						nToExpand++;
						vSizeAndPointerToNode.push_back(std::make_pair(n3.vKeys.size(), &lNodes.front()));
						lNodes.front().lit = lNodes.begin();
					}
				}
				if (n4.vKeys.size()>0)
				{
					lNodes.push_front(n4);
					if (n4.vKeys.size()>1)
					{
						nToExpand++;
						vSizeAndPointerToNode.push_back(std::make_pair(n4.vKeys.size(), &lNodes.front()));
						lNodes.front().lit = lNodes.begin();
					}
				}

				lit = lNodes.erase(lit);
				continue;
			}
		}

		// Finish if there are more nodes than required features
		// or all nodes contain just one point
		if ((int)lNodes.size() >= N || (int)lNodes.size() == prevSize)
		{
			bFinish = true;
		}
		else if (((int)lNodes.size() + nToExpand * 3)>N)
		{

			while (!bFinish)
			{

				prevSize = lNodes.size();

				std::vector<std::pair<int, ExtractorNode*> > vPrevSizeAndPointerToNode = vSizeAndPointerToNode;
				vSizeAndPointerToNode.clear();

				sort(vPrevSizeAndPointerToNode.begin(), vPrevSizeAndPointerToNode.end());
				for (int j = vPrevSizeAndPointerToNode.size() - 1; j >= 0; j--)
				{
					ExtractorNode n1, n2, n3, n4;
					vPrevSizeAndPointerToNode[j].second->DivideNode(n1, n2, n3, n4);

					// Add childs if they contain points
					if (n1.vKeys.size()>0)
					{
						lNodes.push_front(n1);
						if (n1.vKeys.size()>1)
						{
							vSizeAndPointerToNode.push_back(std::make_pair(n1.vKeys.size(), &lNodes.front()));
							lNodes.front().lit = lNodes.begin();
						}
					}
					if (n2.vKeys.size()>0)
					{
						lNodes.push_front(n2);
						if (n2.vKeys.size()>1)
						{
							vSizeAndPointerToNode.push_back(std::make_pair(n2.vKeys.size(), &lNodes.front()));
							lNodes.front().lit = lNodes.begin();
						}
					}
					if (n3.vKeys.size()>0)
					{
						lNodes.push_front(n3);
						if (n3.vKeys.size()>1)
						{
							vSizeAndPointerToNode.push_back(std::make_pair(n3.vKeys.size(), &lNodes.front()));
							lNodes.front().lit = lNodes.begin();
						}
					}
					if (n4.vKeys.size()>0)
					{
						lNodes.push_front(n4);
						if (n4.vKeys.size()>1)
						{
							vSizeAndPointerToNode.push_back(std::make_pair(n4.vKeys.size(), &lNodes.front()));
							lNodes.front().lit = lNodes.begin();
						}
					}

					lNodes.erase(vPrevSizeAndPointerToNode[j].second->lit);

					if ((int)lNodes.size() >= N)
						break;
				}

				if ((int)lNodes.size() >= N || (int)lNodes.size() == prevSize)
					bFinish = true;

			}
		}
	}

	// Retain the best point in each node
	std::vector<cv::KeyPoint> vResultKeys;
	vResultKeys.reserve(nfeatures);
	for (std::list<ExtractorNode>::iterator lit = lNodes.begin(); lit != lNodes.end(); lit++)
	{
		std::vector<cv::KeyPoint> &vNodeKeys = lit->vKeys;
		cv::KeyPoint* pKP = &vNodeKeys[0];
		float maxResponse = pKP->response;

		for (size_t k = 1; k<vNodeKeys.size(); k++)
		{
			if (vNodeKeys[k].response>maxResponse)
			{
				pKP = &vNodeKeys[k];
				maxResponse = vNodeKeys[k].response;
			}
		}

		vResultKeys.push_back(*pKP);
	}

	return vResultKeys;
}

void ORBextractor::ComputeKeyPointsOctTree(std::vector<std::vector<cv::KeyPoint> >& allKeypoints)
{
	allKeypoints.resize(nlevels);

	const float W = 30;

	for (int level = 0; level < nlevels; ++level)
	{
		const int minBorderX = EDGE_THRESHOLD - 3;
		const int minBorderY = minBorderX;
		const int maxBorderX = mvImagePyramid[level].cols - EDGE_THRESHOLD + 3;
		const int maxBorderY = mvImagePyramid[level].rows - EDGE_THRESHOLD + 3;

		std::vector<cv::KeyPoint> vToDistributeKeys;
		vToDistributeKeys.reserve(nfeatures * 10);

		const float width = (maxBorderX - minBorderX);
		const float height = (maxBorderY - minBorderY);

		const int nCols = width / W;
		const int nRows = height / W;
		const int wCell = ceil(width / nCols);
		const int hCell = ceil(height / nRows);

		for (int i = 0; i<nRows; i++)
		{
			const float iniY = minBorderY + i*hCell;
			float maxY = iniY + hCell + 6;

			if (iniY >= maxBorderY - 3)
				continue;
			if (maxY>maxBorderY)
				maxY = maxBorderY;

			for (int j = 0; j<nCols; j++)
			{
				const float iniX = minBorderX + j*wCell;
				float maxX = iniX + wCell + 6;
				if (iniX >= maxBorderX - 6)
					continue;
				if (maxX>maxBorderX)
					maxX = maxBorderX;

				std::vector<cv::KeyPoint> vKeysCell;
				FAST(mvImagePyramid[level].rowRange(iniY, maxY).colRange(iniX, maxX),
					vKeysCell, iniThFAST, true);

				if (vKeysCell.empty())
				{
                    FAST(mvImagePyramid[level].rowRange(iniY, maxY).colRange(iniX, maxX),
						vKeysCell, minThFAST, true);
				}

				if (!vKeysCell.empty())
				{
					for (std::vector<cv::KeyPoint>::iterator vit = vKeysCell.begin(); vit != vKeysCell.end(); vit++)
					{
						(*vit).pt.x += j*wCell;
						(*vit).pt.y += i*hCell;
						vToDistributeKeys.push_back(*vit);
					}
				}

			}
		}

		std::vector<cv::KeyPoint> & keypoints = allKeypoints[level];
		keypoints.reserve(nfeatures);
		//keypoints = vToDistributeKeys;
		//continue;

		keypoints = DistributeOctTree(vToDistributeKeys, minBorderX, maxBorderX,
			minBorderY, maxBorderY, mnFeaturesPerLevel[level], level);

		const int scaledPatchSize = PATCH_SIZE*mvScaleFactor[level];

		// Add border to coordinates and scale information
		const int nkps = keypoints.size();
		for (int i = 0; i<nkps; i++)
		{
			keypoints[i].pt.x += minBorderX;
			keypoints[i].pt.y += minBorderY;
			keypoints[i].octave = level;
			keypoints[i].size = scaledPatchSize;
		}
	}

	// compute orientations
	for (int level = 0; level < nlevels; ++level)
		computeOrientation(mvImagePyramid[level], allKeypoints[level], umax);
}

void ORBextractor::ComputeKeyPointsOld(std::vector<std::vector<cv::KeyPoint> > &allKeypoints)
{
	allKeypoints.resize(nlevels);

	float imageRatio = (float)mvImagePyramid[0].cols / mvImagePyramid[0].rows;

	for (int level = 0; level < nlevels; ++level)
	{
		const int nDesiredFeatures = mnFeaturesPerLevel[level];

		const int levelCols = sqrt((float)nDesiredFeatures / (5 * imageRatio));
		const int levelRows = imageRatio*levelCols;

		const int minBorderX = EDGE_THRESHOLD;
		const int minBorderY = minBorderX;
		const int maxBorderX = mvImagePyramid[level].cols - EDGE_THRESHOLD;
		const int maxBorderY = mvImagePyramid[level].rows - EDGE_THRESHOLD;

		const int W = maxBorderX - minBorderX;
		const int H = maxBorderY - minBorderY;
		const int cellW = ceil((float)W / levelCols);
		const int cellH = ceil((float)H / levelRows);

		const int nCells = levelRows*levelCols;
		const int nfeaturesCell = ceil((float)nDesiredFeatures / nCells);

		std::vector<std::vector<std::vector<cv::KeyPoint> > > cellKeyPoints(levelRows, std::vector<std::vector<cv::KeyPoint> >(levelCols));

		std::vector<std::vector<int> > nToRetain(levelRows, std::vector<int>(levelCols, 0));
		std::vector<std::vector<int> > nTotal(levelRows, std::vector<int>(levelCols, 0));
		std::vector<std::vector<bool> > bNoMore(levelRows, std::vector<bool>(levelCols, false));
		std::vector<int> iniXCol(levelCols);
		std::vector<int> iniYRow(levelRows);
		int nNoMore = 0;
		int nToDistribute = 0;


		float hY = cellH + 6;

		for (int i = 0; i<levelRows; i++)
		{
			const float iniY = minBorderY + i*cellH - 3;
			iniYRow[i] = iniY;

			if (i == levelRows - 1)
			{
				hY = maxBorderY + 3 - iniY;
				if (hY <= 0)
					continue;
			}

			float hX = cellW + 6;

			for (int j = 0; j<levelCols; j++)
			{
				float iniX;

				if (i == 0)
				{
					iniX = minBorderX + j*cellW - 3;
					iniXCol[j] = iniX;
				}
				else
				{
					iniX = iniXCol[j];
				}


				if (j == levelCols - 1)
				{
					hX = maxBorderX + 3 - iniX;
					if (hX <= 0)
						continue;
				}


				cv::Mat cellImage = mvImagePyramid[level].rowRange(iniY, iniY + hY).colRange(iniX, iniX + hX);

				cellKeyPoints[i][j].reserve(nfeaturesCell * 5);

				FAST(cellImage, cellKeyPoints[i][j], iniThFAST, true);

				if (cellKeyPoints[i][j].size() <= 3)
				{
					cellKeyPoints[i][j].clear();

					FAST(cellImage, cellKeyPoints[i][j], minThFAST, true);
				}


				const int nKeys = cellKeyPoints[i][j].size();
				nTotal[i][j] = nKeys;

				if (nKeys>nfeaturesCell)
				{
					nToRetain[i][j] = nfeaturesCell;
					bNoMore[i][j] = false;
				}
				else
				{
					nToRetain[i][j] = nKeys;
					nToDistribute += nfeaturesCell - nKeys;
					bNoMore[i][j] = true;
					nNoMore++;
				}

			}
		}


		// Retain by score

		while (nToDistribute>0 && nNoMore<nCells)
		{
			int nNewFeaturesCell = nfeaturesCell + ceil((float)nToDistribute / (nCells - nNoMore));
			nToDistribute = 0;

			for (int i = 0; i<levelRows; i++)
			{
				for (int j = 0; j<levelCols; j++)
				{
					if (!bNoMore[i][j])
					{
						if (nTotal[i][j]>nNewFeaturesCell)
						{
							nToRetain[i][j] = nNewFeaturesCell;
							bNoMore[i][j] = false;
						}
						else
						{
							nToRetain[i][j] = nTotal[i][j];
							nToDistribute += nNewFeaturesCell - nTotal[i][j];
							bNoMore[i][j] = true;
							nNoMore++;
						}
					}
				}
			}
		}

		std::vector<cv::KeyPoint> & keypoints = allKeypoints[level];
		keypoints.reserve(nDesiredFeatures * 2);

		const int scaledPatchSize = PATCH_SIZE*mvScaleFactor[level];

		// Retain by score and transform coordinates
		for (int i = 0; i<levelRows; i++)
		{
			for (int j = 0; j<levelCols; j++)
			{
				std::vector<cv::KeyPoint> &keysCell = cellKeyPoints[i][j];
				cv::KeyPointsFilter::retainBest(keysCell, nToRetain[i][j]);
				if ((int)keysCell.size()>nToRetain[i][j])
					keysCell.resize(nToRetain[i][j]);


				for (size_t k = 0, kend = keysCell.size(); k<kend; k++)
				{
					keysCell[k].pt.x += iniXCol[j];
					keysCell[k].pt.y += iniYRow[i];
					keysCell[k].octave = level;
					keysCell[k].size = scaledPatchSize;
					keypoints.push_back(keysCell[k]);
				}
			}
		}

		if ((int)keypoints.size()>nDesiredFeatures)
		{
			cv::KeyPointsFilter::retainBest(keypoints, nDesiredFeatures);
			keypoints.resize(nDesiredFeatures);
		}
	}

	// and compute orientations
	for (int level = 0; level < nlevels; ++level)
		computeOrientation(mvImagePyramid[level], allKeypoints[level], umax);
}

static void computeDescriptors(const cv::Mat& image, std::vector<cv::KeyPoint>& keypoints, cv::Mat& descriptors,
	const std::vector<cv::Point>& pattern)
{
	descriptors = cv::Mat::zeros((int)keypoints.size(), 32, CV_8UC1);

	for (size_t i = 0; i < keypoints.size(); i++)
		computeOrbDescriptor(keypoints[i], image, &pattern[0], descriptors.ptr((int)i));
}

void ORBextractor::operator()(cv::InputArray _image, cv::InputArray _mask, std::vector<cv::KeyPoint>& _keypoints,
	cv::OutputArray _descriptors)
{
	if (_image.empty())
		return;

	cv::Mat image = _image.getMat();
	assert(image.type() == CV_8UC1);

	// Pre-compute the scale pyramid
	ComputePyramid(image);

	std::vector<std::vector<cv::KeyPoint> > allKeypoints;
	ComputeKeyPointsOctTree(allKeypoints);
	//ComputeKeyPointsOld(allKeypoints);

	cv::Mat descriptors;

	int nkeypoints = 0;
	for (int level = 0; level < nlevels; ++level)
		nkeypoints += (int)allKeypoints[level].size();
	if (nkeypoints == 0)
		_descriptors.release();
	else
	{
		_descriptors.create(nkeypoints, 32, CV_8U);
		descriptors = _descriptors.getMat();
	}

	_keypoints.clear();
	_keypoints.reserve(nkeypoints);

	int offset = 0;
	for (int level = 0; level < nlevels; ++level)
	{
		std::vector<cv::KeyPoint>& keypoints = allKeypoints[level];
		int nkeypointsLevel = (int)keypoints.size();

		if (nkeypointsLevel == 0)
			continue;

		// preprocess the resized image
		cv::Mat workingMat = mvImagePyramid[level].clone();
		GaussianBlur(workingMat, workingMat, cv::Size(7, 7), 2, 2, cv::BORDER_REFLECT_101);

		// Compute the descriptors
		cv::Mat desc = descriptors.rowRange(offset, offset + nkeypointsLevel);
		computeDescriptors(workingMat, keypoints, desc, pattern);

		offset += nkeypointsLevel;

		// Scale keypoint coordinates
		if (level != 0)
		{
			float scale = mvScaleFactor[level]; //getScale(level, firstLevel, scaleFactor);
			for (std::vector<cv::KeyPoint>::iterator keypoint = keypoints.begin(),
				keypointEnd = keypoints.end(); keypoint != keypointEnd; ++keypoint)
				keypoint->pt *= scale;
		}
		// And add the keypoints to the output
		_keypoints.insert(_keypoints.end(), keypoints.begin(), keypoints.end());
	}
}

void ORBextractor::ComputePyramid(cv::Mat image)
{
	for (int level = 0; level < nlevels; ++level)
	{
		float scale = mvInvScaleFactor[level];
		cv::Size sz(cvRound((float)image.cols*scale), cvRound((float)image.rows*scale));
		cv::Size wholeSize(sz.width + EDGE_THRESHOLD * 2, sz.height + EDGE_THRESHOLD * 2);
		cv::Mat temp(wholeSize, image.type()), masktemp;
		mvImagePyramid[level] = temp(cv::Rect(EDGE_THRESHOLD, EDGE_THRESHOLD, sz.width, sz.height));

		// Compute the resized image
		if (level != 0)
		{
			resize(mvImagePyramid[level - 1], mvImagePyramid[level], sz, 0, 0, cv::INTER_LINEAR);

			copyMakeBorder(mvImagePyramid[level], temp, EDGE_THRESHOLD, EDGE_THRESHOLD, EDGE_THRESHOLD, EDGE_THRESHOLD,
				cv::BORDER_REFLECT_101 + cv::BORDER_ISOLATED);
		}
		else
		{
			copyMakeBorder(image, temp, EDGE_THRESHOLD, EDGE_THRESHOLD, EDGE_THRESHOLD, EDGE_THRESHOLD,
				cv::BORDER_REFLECT_101);
		}
	}

}
